Currently submitted to: Journal of Medical Internet Research
Date Submitted: May 13, 2026
Open Peer Review Period: May 14, 2026 - Jul 9, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
From prediction model to clinical decision support: a user-centered study of anesthesiologists’ requirements for perioperative clinical decision support systems
ABSTRACT
Background:
High-performing perioperative prediction models have not consistently translated into clinical benefit, in part because model outputs must be delivered through clinical decision support systems (CDSS) that align with anesthesia workflows and end-user needs.
Objective:
To identify anesthesia professionals’ requirements for perioperative CDSS and use these findings to inform the design specification of a user-centered perioperative CDSS.
Methods:
This user-centered study was conducted in four sequential phases: translation of a previously validated explainable machine-learning model into candidate CDSS functions; three rounds of focus group–based iterative prototyping; a nationwide cross-sectional questionnaire survey; and CDSS finalization based on iterative prototyping and survey findings. The survey assessed requirements for information display, alerting, explainability, intervention support, and workflow integration among anesthesia-related professionals in China.
Results:
Three rounds of focus group discussion and iterative prototyping generated a preliminary prototype comprising candidate modules for information display, alerting, explainability, intervention support, and workflow integration. A total of 2401 valid questionnaires were analyzed. Respondents generally preferred direct risk presentation, probability-based alerting, interpretable displays of modifiable risk factors, actionable intervention support, and integration within existing clinical platforms. These findings informed the final specification of an integrated CDSS within the anesthesia information system, including dynamic risk prediction, threshold-based alerting, explainable risk attribution, and evidence-informed intervention recommendations.
Conclusions:
In this user-centered design study, anesthesia professionals identified key requirements for perioperative CDSS, including direct information display, clinically meaningful alerts, explainable risk-factor presentation, actionable recommendations, and workflow integration. These findings may inform the translation of perioperative prediction models into decision support tools that are more usable and acceptable in routine anesthesia practice.
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